reddit_stock_analyzer/rstat_tool/main.py

# rstat_tool/main.py

import argparse
import json
import os
import time
import sys
import subprocess
from dotenv import load_dotenv
from pathlib import Path

import praw

from . import database
from .ticker_extractor import extract_tickers
from .sentiment_analyzer import get_sentiment_score
from .logger_setup import setup_logging, logger as log


def load_subreddits(filepath):
    """Loads a list of subreddits from a JSON file."""
    try:
        with open(filepath, "r") as f:
            return json.load(f).get("subreddits", [])
    except (FileNotFoundError, json.JSONDecodeError) as e:
        log.error(f"Error loading config file '{filepath}': {e}")
        return None


def get_reddit_instance():
    """Initializes and returns a PRAW Reddit instance."""
    env_path = Path(__file__).parent.parent / ".env"
    load_dotenv(dotenv_path=env_path)

    client_id = os.getenv("REDDIT_CLIENT_ID")
    client_secret = os.getenv("REDDIT_CLIENT_SECRET")
    user_agent = os.getenv("REDDIT_USER_AGENT")
    if not all([client_id, client_secret, user_agent]):
        log.error("Error: Reddit API credentials not found in .env file.")
        return None
    return praw.Reddit(
        client_id=client_id, client_secret=client_secret, user_agent=user_agent
    )


def get_financial_data_via_fetcher(ticker_symbol):
    """
    Calls two separate, isolated fetcher scripts to get market cap and closing price,
    bypassing the internal library conflict.
    """
    financials = {"market_cap": None, "closing_price": None}
    project_root = Path(__file__).parent.parent

    # --- Call 1: Get Market Cap ---
    try:
        mc_script_path = project_root / "fetch_market_cap.py"
        command_mc = [sys.executable, str(mc_script_path), ticker_symbol]
        result_mc = subprocess.run(
            command_mc, capture_output=True, text=True, check=True, timeout=30
        )
        financials.update(json.loads(result_mc.stdout))
    except Exception as e:
        log.warning(f"Market cap fetcher failed for {ticker_symbol}: {e}")

    # --- Call 2: Get Closing Price ---
    try:
        cp_script_path = project_root / "fetch_close_price.py"
        command_cp = [sys.executable, str(cp_script_path), ticker_symbol]
        result_cp = subprocess.run(
            command_cp, capture_output=True, text=True, check=True, timeout=30
        )
        financials.update(json.loads(result_cp.stdout))
    except Exception as e:
        log.warning(f"Closing price fetcher failed for {ticker_symbol}: {e}")

    return financials


# --- HELPER FUNCTION: Contains all the optimized logic for one post ---
def _process_submission(
    submission, subreddit_id, conn, comment_limit, fetch_financials
):
    """
    Processes a single Reddit submission with optimized logic.
    - Uses a single loop over comments.
    - Caches ticker IDs to reduce DB lookups.
    """
    current_time = time.time()

    # 1. Initialize data collectors for this post
    tickers_in_title = set(extract_tickers(submission.title))
    all_tickers_found_in_post = set(tickers_in_title)
    all_comment_sentiments = []
    ticker_id_cache = {}  # In-memory cache for ticker IDs for this post

    submission.comments.replace_more(limit=0)
    all_comments = submission.comments.list()[:comment_limit]

    # 2. --- SINGLE LOOP OVER COMMENTS ---
    # We gather all necessary information in one pass.
    for comment in all_comments:
        comment_sentiment = get_sentiment_score(comment.body)
        all_comment_sentiments.append(comment_sentiment)  # For the deep dive

        tickers_in_comment = set(extract_tickers(comment.body))
        if not tickers_in_comment:
            continue

        all_tickers_found_in_post.update(tickers_in_comment)

        # Apply the hybrid logic
        if tickers_in_title:
            # If the title has tickers, every comment is a mention for them
            for ticker_symbol in tickers_in_title:
                if ticker_symbol not in ticker_id_cache:
                    ticker_id_cache[ticker_symbol] = database.get_or_create_entity(
                        conn, "tickers", "symbol", ticker_symbol
                    )
                ticker_id = ticker_id_cache[ticker_symbol]
                database.add_mention(
                    conn,
                    ticker_id,
                    subreddit_id,
                    submission.id,
                    "comment",
                    int(comment.created_utc),
                    comment_sentiment,
                )
        else:
            # If no title tickers, only direct mentions in comments count
            for ticker_symbol in tickers_in_comment:
                if ticker_symbol not in ticker_id_cache:
                    ticker_id_cache[ticker_symbol] = database.get_or_create_entity(
                        conn, "tickers", "symbol", ticker_symbol
                    )
                ticker_id = ticker_id_cache[ticker_symbol]
                database.add_mention(
                    conn,
                    ticker_id,
                    subreddit_id,
                    submission.id,
                    "comment",
                    int(comment.created_utc),
                    comment_sentiment,
                )

    # 3. Process title mentions (if any)
    if tickers_in_title:
        log.info(
            f"  -> Title Mention(s): {', '.join(tickers_in_title)}. Attributing all comments."
        )
        post_sentiment = get_sentiment_score(submission.title)
        for ticker_symbol in tickers_in_title:
            if ticker_symbol not in ticker_id_cache:
                ticker_id_cache[ticker_symbol] = database.get_or_create_entity(
                    conn, "tickers", "symbol", ticker_symbol
                )
            ticker_id = ticker_id_cache[ticker_symbol]
            database.add_mention(
                conn,
                ticker_id,
                subreddit_id,
                submission.id,
                "post",
                int(submission.created_utc),
                post_sentiment,
            )

    # 4. Fetch financial data if enabled
    if fetch_financials:
        for ticker_symbol in all_tickers_found_in_post:
            ticker_id = ticker_id_cache[ticker_symbol]  # Guaranteed to be in cache
            ticker_info = database.get_ticker_info(conn, ticker_id)
            if not ticker_info["last_updated"] or (
                current_time - ticker_info["last_updated"]
                > database.MARKET_CAP_REFRESH_INTERVAL
            ):
                log.info(f"    -> Fetching financial data for {ticker_symbol}...")
                financials = get_financial_data_via_fetcher(ticker_symbol)
                database.update_ticker_financials(
                    conn,
                    ticker_id,
                    financials.get("market_cap"),
                    financials.get("closing_price"),
                )

    # 5. Save deep dive analysis
    avg_sentiment = (
        sum(all_comment_sentiments) / len(all_comment_sentiments)
        if all_comment_sentiments
        else 0
    )
    post_analysis_data = {
        "post_id": submission.id,
        "title": submission.title,
        "post_url": f"https://reddit.com{submission.permalink}",
        "subreddit_id": subreddit_id,
        "post_timestamp": int(submission.created_utc),
        "comment_count": len(all_comments),
        "avg_comment_sentiment": avg_sentiment,
    }
    database.add_or_update_post_analysis(conn, post_analysis_data)


def scan_subreddits(
    reddit,
    subreddits_list,
    post_limit=100,
    comment_limit=100,
    days_to_scan=1,
    fetch_financials=True,
):
    conn = database.get_db_connection()
    post_age_limit = days_to_scan * 86400
    current_time = time.time()

    log.info(
        f"Scanning {len(subreddits_list)} subreddit(s) for NEW posts in the last {days_to_scan} day(s)..."
    )
    if not fetch_financials:
        log.warning("NOTE: Financial data fetching is disabled for this run.")

    for subreddit_name in subreddits_list:
        try:
            normalized_sub_name = subreddit_name.lower()
            subreddit_id = database.get_or_create_entity(
                conn, "subreddits", "name", normalized_sub_name
            )
            subreddit = reddit.subreddit(normalized_sub_name)
            log.info(f"Scanning r/{normalized_sub_name}...")

            for submission in subreddit.new(limit=post_limit):
                if (current_time - submission.created_utc) > post_age_limit:
                    log.info(
                        f"  -> Reached posts older than the {days_to_scan}-day limit."
                    )
                    break

                # Call the new helper function for each post
                _process_submission(
                    submission, subreddit_id, conn, comment_limit, fetch_financials
                )

        except Exception as e:
            log.error(
                f"Could not scan r/{normalized_sub_name}. Error: {e}", exc_info=True
            )

    conn.close()
    log.critical("\n--- Scan Complete ---")


def main():
    """Main function to run the Reddit stock analysis tool."""
    parser = argparse.ArgumentParser(
        description="Analyze stock ticker mentions on Reddit.",
        formatter_class=argparse.RawTextHelpFormatter,
    )

    parser.add_argument(
        "-f",
        "--config",
        default="subreddits.json",
        help="Path to the JSON file for scanning. (Default: subreddits.json)",
    )
    parser.add_argument(
        "-s", "--subreddit", help="Scan a single subreddit, ignoring the config file."
    )
    parser.add_argument(
        "-d",
        "--days",
        type=int,
        default=1,
        help="Number of past days to scan for new posts. (Default: 1)",
    )
    parser.add_argument(
        "-p",
        "--posts",
        type=int,
        default=200,
        help="Max posts to check per subreddit. (Default: 200)",
    )
    parser.add_argument(
        "-c",
        "--comments",
        type=int,
        default=100,
        help="Number of comments to scan per post. (Default: 100)",
    )
    parser.add_argument(
        "-n",
        "--no-financials",
        action="store_true",
        help="Disable fetching of financial data during the Reddit scan.",
    )
    parser.add_argument(
        "--update-top-tickers",
        action="store_true",
        help="Update financial data only for tickers currently in the Top 10 daily/weekly dashboards.",
    )
    parser.add_argument(
        "-u",
        "--update-financials-only",
        nargs="?",
        const="ALL_TICKERS",  # A special value to signify "update all"
        default=None,
        metavar="TICKER",
        help="Update financials. Provide a ticker symbol to update just one,\nor use the flag alone to update all tickers in the database.",
    )
    parser.add_argument(
        "--debug",
        action="store_true",
        help="Enable detailed debug logging to the console.",
    )
    parser.add_argument(
        "--stdout", action="store_true", help="Print all log messages to the console."
    )

    args = parser.parse_args()
    setup_logging(console_verbose=args.stdout, debug_mode=args.debug)

    database.initialize_db()

    if args.update_top_tickers:
        log.critical("--- Starting Financial Data Update for Top Tickers ---")

        # 1. Start with an empty set to hold all unique tickers
        tickers_to_update = set()

        # 2. Get the overall top tickers
        log.info("-> Checking overall top daily and weekly tickers...")
        top_daily_overall = database.get_top_daily_ticker_symbols()
        top_weekly_overall = database.get_top_weekly_ticker_symbols()
        tickers_to_update.update(top_daily_overall)
        tickers_to_update.update(top_weekly_overall)

        # 3. Get all subreddits and loop through them
        all_subreddits = database.get_all_scanned_subreddits()
        log.info(
            f"-> Checking top tickers for {len(all_subreddits)} individual subreddit(s)..."
        )
        for sub_name in all_subreddits:
            log.debug(f"  -> Checking r/{sub_name}...")
            top_daily_sub = database.get_top_daily_ticker_symbols_for_subreddit(
                sub_name
            )
            top_weekly_sub = database.get_top_weekly_ticker_symbols_for_subreddit(
                sub_name
            )
            tickers_to_update.update(top_daily_sub)
            tickers_to_update.update(top_weekly_sub)

        unique_top_tickers = sorted(list(tickers_to_update))

        if not unique_top_tickers:
            log.info("No top tickers found in the last week. Nothing to update.")
        else:
            log.info(
                f"Found {len(unique_top_tickers)} unique top tickers to update: {', '.join(unique_top_tickers)}"
            )
            conn = database.get_db_connection()
            for ticker_symbol in unique_top_tickers:
                # 4. Find the ticker's ID to perform the update
                ticker_info = database.get_ticker_by_symbol(ticker_symbol)
                if ticker_info:
                    log.info(f"  -> Updating financials for {ticker_info['symbol']}...")
                    financials = get_financial_data_via_fetcher(ticker_info["symbol"])
                    database.update_ticker_financials(
                        conn,
                        ticker_info["id"],
                        financials.get("market_cap"),
                        financials.get("closing_price"),
                    )
            conn.close()

        log.critical("--- Top Ticker Financial Data Update Complete ---")

    elif args.update_financials_only:
        # --- Mode 2: Update All or a Single Ticker ---
        update_mode = args.update_financials_only
        if update_mode == "ALL_TICKERS":
            log.critical("--- Starting Financial Data Update for ALL tickers ---")
            all_tickers = database.get_all_tickers()
            log.info(f"Found {len(all_tickers)} tickers in the database to update.")
            conn = database.get_db_connection()
            for ticker in all_tickers:
                symbol = ticker["symbol"]
                log.info(f"  -> Updating financials for {symbol}...")
                financials = get_financial_data_via_fetcher(symbol)
                database.update_ticker_financials(
                    conn,
                    ticker["id"],
                    financials.get("market_cap"),
                    financials.get("closing_price"),
                )
            conn.close()
        else:
            ticker_symbol_to_update = update_mode
            log.critical(
                f"--- Starting Financial Data Update for single ticker: {ticker_symbol_to_update} ---"
            )
            ticker_info = database.get_ticker_by_symbol(ticker_symbol_to_update)
            if ticker_info:
                conn = database.get_db_connection()
                log.info(f"  -> Updating financials for {ticker_info['symbol']}...")
                financials = get_financial_data_via_fetcher(ticker_info["symbol"])
                database.update_ticker_financials(
                    conn,
                    ticker_info["id"],
                    financials.get("market_cap"),
                    financials.get("closing_price"),
                )
                conn.close()
            else:
                log.error(
                    f"Ticker '{ticker_symbol_to_update}' not found in the database."
                )
        log.critical("--- Financial Data Update Complete ---")

    else:
        # --- Mode 3: Default Reddit Scan ---
        log.critical("--- Starting Reddit Scan Mode ---")
        if args.subreddit:
            subreddits_to_scan = [args.subreddit]
            log.info(f"Targeted Scan Mode: Focusing on r/{args.subreddit}")
        else:
            log.info(f"Config Scan Mode: Loading subreddits from {args.config}")
            subreddits_to_scan = load_subreddits(args.config)

        if not subreddits_to_scan:
            log.error("Error: No subreddits to scan.")
            return

        reddit = get_reddit_instance()
        if not reddit:
            return

        scan_subreddits(
            reddit,
            subreddits_to_scan,
            post_limit=args.posts,
            comment_limit=args.comments,
            days_to_scan=args.days,
            fetch_financials=(not args.no_financials),
        )


if __name__ == "__main__":
    main()